Baselines. We compare against multiple vision-based approaches that learn rewards in a self-supervised manner: (1) XIRL [1]} that learns a reward function by applying the TCC [2]} loss on demonstration video sequences, (2) TCN [3]} which is a self-supervised contrastive method for video representation learning that optimizes for temporally disentangled representations, and (3) LIFS [4]} that learns an invariant feature space using a dynamic time warping-based contrastive loss. Lastly, we also compare against the manually designed (4) Environment Rewards from [5]}. For vision-based baselines, we use a ResNet-18 encoder pretrained on ImageNet [6]} classification. We use the hyperparameters, data augmentation schemes and network architectures provided in [1]} for all vision-based baselines. Please refer to Appendix REF 
for description of environment rewards and [1]} for details on the vision-based baselines.
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